Objects with thin features and fine details are challenging for most multi-view stereo techniques, since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pixel depth values by exploiting local gradient information in densely sampled light fields. This approach can generate accurate depth values for very thin features, and can be run for each pixel in parallel. We assess the reliability of our depth estimates using a novel two-sided photoconsistency measure, which can capture whether the pixel lies on a texture or a silhouette edge. This information is then used to propagate the depth estimates at high gradient regions to smooth parts of the views efficiently and reliably using edge-aware filtering. In the last step, the per-image depth values and color information are aggregated in 3D space using a voting scheme, allowing the reconstruction of a globally consistent mesh for the object. Our approach can process large video datasets very efficiently and at the same time generates high quality object reconstructions that compare favorably to the results of state-of-the-art multi-view stereo methods.

Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our article is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, for example, captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation in both 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer. We demonstrate how the results of our method can be used for considerably improving the speed and quality of image-based 3D reconstruction algorithms, and we compare our results to state-of-the-art segmentation and multiview stereo methods.

We present a method for consistent automatic transfer of edits applied to one image to many other images of the same object or scene. By introducing novel, content-adaptive weight functions we enhance the non-rigid alignment framework of Lucas-Kanade to robustly handle changes of view point, illumination and non-rigid deformations of the subjects. Our weight functions are content-aware and possess high-order smoothness, enabling to define high-quality image warping with a low number of parameters using spatially-varying weighted combinations of affine deformations. Optimizing the warp parameters leads to subpixel-accurate alignment while maintaining computation efficiency. Our method allows users to perform precise, localized edits such as simultaneous painting on multiple images in real-time, relieving them from tedious and repetitive manual reapplication to each individual image.